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ORCIDS. Shankar Sastry
Shankar Sastry 0001 – Shankar S. Sastry
Person information
- affiliation: University of California at Berkeley, Department of Electrical Engineering and Computer Sciences, CA, USA
Other persons with the same name
- Shankar Sastry — disambiguation page
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2020 – today
- 2023
[c310]Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su, Shankar Sastry:
Dynamic Tolling in Arc-based Traffic Assignment Models. Allerton 2023: 1-8- [c309]Sheng-Jung Yu
, Inigo Incer, Valmik Prabhu, Anwesha Chattoraj, Eric Vin, Daniel J. Fremont, Ankur Mehta, Alberto L. Sangiovanni-Vincentelli, Shankar Sastry, Sanjit A. Seshia:
Symbiotic CPS Design-Space Exploration through Iterated Optimization. CPS-IoT Week Workshops 2023: 92-99 - [c308]Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar, Shankar S. Sastry:
Competing Bandits in Time Varying Matching Markets. L4DC 2023: 1020-1031 - [i78]Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar:
Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. CoRR abs/2302.01421 (2023) - [i77]Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su, S. Shankar Sastry:
Arc-based Traffic Assignment: Equilibrium Characterization and Learning. CoRR abs/2304.04705 (2023) - [i76]Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma:
Representation Learning via Manifold Flattening and Reconstruction. CoRR abs/2305.01777 (2023) - [i75]Xin Guo, Xinyu Li, Chinmay Maheshwari, Shankar Sastry, Manxi Wu:
Markov α-Potential Games: Equilibrium Approximation and Regret Analysis. CoRR abs/2305.12553 (2023) - [i74]Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su, Shankar Sastry:
Dynamic Tolling in Arc-based Traffic Assignment Models. CoRR abs/2307.05466 (2023) - 2022
- [j112]Sanjit A. Seshia, Dorsa Sadigh, S. Shankar Sastry:
Toward verified artificial intelligence. Commun. ACM 65(7): 46-55 (2022) - [j111]Amay Saxena, Chih-Yuan Chiu, Ritika Shrivastava, Joseph Menke, Shankar Sastry:
Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization. IEEE Robotics Autom. Lett. 7(3): 7148-7155 (2022) - [c307]Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff:
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. AISTATS 2022: 6702-6734 - [c306]Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, S. Shankar Sastry:
Dynamic Tolling for Inducing Socially Optimal Traffic Loads. ACC 2022: 4601-4607 - [c305]Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, S. Shankar Sastry:
Inducing Social Optimality in Games via Adaptive Incentive Design. CDC 2022: 2864-2869 - [c304]Tyler Westenbroek, Anand Siththaranjan, Mohsin Sarwari, Claire J. Tomlin, S. Shankar Sastry:
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control. CDC 2022: 7423-7430 - [c303]Tyler Westenbroek, Fernando Castañeda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath:
Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning. CoRL 2022: 2125-2135 - [c302]Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. NeurIPS 2022 - [i73]Victoria Tuck, Yash Vardhan Pant, Sanjit A. Seshia, S. Shankar Sastry:
DEC-LOS-RRT: Decentralized Path Planning for Multi-robot Systems with Line-of-sight Constrained Communication. CoRR abs/2203.02609 (2022) - [i72]Tyler Westenbroek, Anand Siththaranjan, Mohsin Sarwari, Claire J. Tomlin, Shankar S. Sastry:
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control. CoRR abs/2204.01986 (2022) - [i71]Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, Shankar Sastry:
Inducing Social Optimality in Games via Adaptive Incentive Design. CoRR abs/2204.05507 (2022) - [i70]Chinmay Maheshwari, Manxi Wu, Druv Pai, Shankar Sastry:
Independent and Decentralized Learning in Markov Potential Games. CoRR abs/2205.14590 (2022) - [i69]Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. CoRR abs/2206.02344 (2022) - [i68]Tyler Westenbroek, Fernando Castañeda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath:
Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning. CoRR abs/2208.06721 (2022) - [i67]Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar, Shankar Sastry:
Competing Bandits in Time Varying Matching Markets. CoRR abs/2210.11692 (2022) - [i66]Chih-Yuan Chiu, Kshitij Kulkarni, S. Shankar Sastry:
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test. CoRR abs/2211.16596 (2022) - [i65]Ritwik Gupta, Alexander M. Bayen, Sarah Rohrschneider, Adrienne Fulk, Andrew Reddie, Sanjit A. Seshia, Shankar Sastry, Janet Napolitano:
Emerging Technology and Policy Co-Design Considerations for the Safe and Transparent Use of Small Unmanned Aerial Systems. CoRR abs/2212.02795 (2022) - 2021
- [c301]Tyler Westenbroek, Ayush Agrawal, Fernando Castañeda, S. Shankar Sastry, Koushil Sreenath:
Combining Model-Based Design and Model-Free Policy Optimization to Learn Safe, Stabilizing Controllers. ADHS 2021: 19-24 - [c300]Tyler Westenbroek, Xiaobin Xiong, S. Shankar Sastry, Aaron D. Ames:
Smooth Approximations for Hybrid Optimal Control Problems with Application to Robotic Walking. ADHS 2021: 181-186 - [c299]Victoria Tuck, Yash Vardhan Pant, Sanjit A. Seshia, S. Shankar Sastry:
DEC-LOS-RRT: Decentralized Path Planning for Multi-robot Systems with Line-of-sight Constrained Communication. CCTA 2021: 103-110 - [c298]David Livingston McPherson, Kaylene C. Stocking, S. Shankar Sastry:
Maximum Likelihood Constraint Inference from Stochastic Demonstrations. CCTA 2021: 1208-1213 - [c297]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC 2021: 742-749 - [c296]David Livingston McPherson, S. Shankar Sastry:
An Efficient Understandability Objective for Dynamic Optimal Control. IROS 2021: 986-992 - [c295]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? NeurIPS 2021: 15257-15269 - [i64]David Livingston McPherson, Kaylene C. Stocking, S. Shankar Sastry:
Maximum Likelihood Constraint Inference from Stochastic Demonstrations. CoRR abs/2102.12554 (2021) - [i63]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR abs/2103.15010 (2021) - [i62]Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff:
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization. CoRR abs/2106.09082 (2021) - [i61]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? CoRR abs/2106.12529 (2021) - [i60]Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, Shankar Sastry:
Dynamic Tolling for Inducing Socially Optimal Traffic Loads. CoRR abs/2110.08879 (2021) - [i59]Amay Saxena, Chih-Yuan Chiu, Joseph Menke, Ritika Shrivastava, Shankar Sastry:
Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization. CoRR abs/2112.05921 (2021) - 2020
- [j110]Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry:
On Gradient-Based Learning in Continuous Games. SIAM J. Math. Data Sci. 2(1): 103-131 (2020) - [j109]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Competitive Statistical Estimation With Strategic Data Sources. IEEE Trans. Autom. Control. 65(4): 1537-1551 (2020) - [c294]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS 2020: 860-868 - [c293]Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry:
Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CDC 2020: 118-125 - [c292]Tyler Westenbroek, Fernando Castañeda, Ayush Agrawal, S. Shankar Sastry, Koushil Sreenath:
Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics. CDC 2020: 737-744 - [c291]Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CDC 2020: 3604-3610 - [c290]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c289]Andreea Bobu, Dexter R. R. Scobee, Jaime F. Fisac, S. Shankar Sastry, Anca D. Dragan:
LESS is More: Rethinking Probabilistic Models of Human Behavior. HRI 2020: 429-437 - [c288]Dexter R. R. Scobee, S. Shankar Sastry:
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. ICLR 2020 - [c287]Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin:
Feedback Linearization for Uncertain Systems via Reinforcement Learning. ICRA 2020: 1364-1371 - [c286]Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Shankar Sastry, Claire J. Tomlin, Koushil Sreenath:
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning. L4DC 2020: 990-999 - [i58]Andreea Bobu, Dexter R. R. Scobee, Jaime F. Fisac, S. Shankar Sastry, Anca D. Dragan:
LESS is More: Rethinking Probabilistic Models of Human Behavior. CoRR abs/2001.04465 (2020) - [i57]Valmik Prabhu, Amay Saxena, S. Shankar Sastry:
Exponentially Stable First Order Control on Matrix Lie Groups. CoRR abs/2004.00239 (2020) - [i56]Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry:
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CoRR abs/2004.02766 (2020) - [i55]Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, S. Shankar Sastry, Claire J. Tomlin, Koushil Sreenath:
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning. CoRR abs/2004.07276 (2020) - [i54]Oladapo Afolabi, Allen Y. Yang, S. Shankar Sastry:
DeepSDF x Sim(3): Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation. CoRR abs/2004.09048 (2020) - [i53]Tyler Westenbroek, Fernando Castañeda, Ayush Agrawal, S. Shankar Sastry, Koushil Sreenath:
Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics. CoRR abs/2004.10331 (2020) - [i52]Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CoRR abs/2010.15599 (2020)
2010 – 2019
- 2019
- [c285]Tyler Westenbroek, Xiaobin Xiong, Aaron D. Ames, S. Shankar Sastry:
Optimal Control of Piecewise-Smooth Control Systems via Singular Perturbations. CDC 2019: 3046-3053 - [c284]Elis Stefansson, Jaime F. Fisac, Dorsa Sadigh, S. Shankar Sastry, Karl Henrik Johansson:
Human-robot interaction for truck platooning using hierarchical dynamic games. ECC 2019: 3165-3172 - [c283]Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, Anca D. Dragan:
Hierarchical Game-Theoretic Planning for Autonomous Vehicles. ICRA 2019: 9590-9596 - [i51]Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry:
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR abs/1901.00838 (2019) - [i50]Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran:
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples. CoRR abs/1901.08360 (2019) - [i49]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Competitive Statistical Estimation with Strategic Data Sources. CoRR abs/1904.12768 (2019) - [i48]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR abs/1907.03712 (2019) - [i47]Dexter R. R. Scobee, S. Shankar Sastry:
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. CoRR abs/1909.05477 (2019) - [i46]Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin:
Feedback Linearization for Unknown Systems via Reinforcement Learning. CoRR abs/1910.13272 (2019) - [i45]Kamil Nar, S. Shankar Sastry:
Persistency of Excitation for Robustness of Neural Networks. CoRR abs/1911.01043 (2019) - 2018
- [j108]Dorsa Sadigh, Nick Landolfi, Shankar S. Sastry, Sanjit A. Seshia, Anca D. Dragan:
Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state. Auton. Robots 42(7): 1405-1426 (2018) - [j107]Roy Dong, Lillian J. Ratliff, Alvaro A. Cárdenas, Henrik Ohlsson, S. Shankar Sastry:
Quantifying the Utility-Privacy Tradeoff in the Internet of Things. ACM Trans. Cyber Phys. Syst. 2(2): 8:1-8:28 (2018) - [j106]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, S. Shankar Sastry, Costas J. Spanos:
A Robust Utility Learning Framework via Inverse Optimization. IEEE Trans. Control. Syst. Technol. 26(3): 954-970 (2018) - [c282]Tyler Westenbroek, Humberto González, S. Shankar Sastry:
A New Solution Concept and Family of Relaxations for Hybrid Dynamical Systems. CDC 2018: 743-750 - [c281]Kamil Nar, S. Shankar Sastry:
An Analytical Framework to Address the Data Exfiltration of Advanced Persistent Threats. CDC 2018: 867-873 - [c280]David Livingston McPherson, Dexter R. R. Scobee, Joseph Menke, Allen Y. Yang, S. Shankar Sastry:
Modeling Supervisor Safe Sets for Improving Collaboration in Human-Robot Teams. IROS 2018: 861-868 - [c279]Oladapo Afolabi, Katherine Rose Driggs-Campbell, Roy Dong, Mykel J. Kochenderfer, S. Shankar Sastry:
People as Sensors: Imputing Maps from Human Actions. IROS 2018: 2342-2348 - [c278]Kamil Nar, Shankar Sastry:
Step Size Matters in Deep Learning. NeurIPS 2018: 3440-3448 - [c277]Dexter R. R. Scobee, Vicenç Rúbies Royo, Claire J. Tomlin, S. Shankar Sastry:
Haptic Assistance via Inverse Reinforcement Learning. SMC 2018: 1510-1517 - [i44]Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths:
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. CoRR abs/1802.01780 (2018) - [i43]Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan:
Generating Plans that Predict Themselves. CoRR abs/1802.05250 (2018) - [i42]Kamil Nar, Shankar Sastry:
Residual Networks: Lyapunov Stability and Convex Decomposition. CoRR abs/1803.08203 (2018) - [i41]David Livingston McPherson, Dexter R. R. Scobee, Joseph Menke, Allen Y. Yang, S. Shankar Sastry:
Modeling Supervisor Safe Sets for Improving Collaboration in Human-Robot Teams. CoRR abs/1805.03328 (2018) - [i40]Kamil Nar, S. Shankar Sastry:
Step Size Matters in Deep Learning. CoRR abs/1805.08890 (2018) - [i39]Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, Anca D. Dragan:
Hierarchical Game-Theoretic Planning for Autonomous Vehicles. CoRR abs/1810.05766 (2018) - 2017
- [c276]Ruoxi Jia, Roy Dong, Prashanth Ganesh, Shankar Sastry, Costas J. Spanos:
Towards a theory of free-lunch privacy in cyber-physical systems. Allerton 2017: 902-910 - [c275]Negar Mehr, Dorsa Sadigh, Roberto Horowitz, S. Shankar Sastry, Sanjit A. Seshia:
Stochastic predictive freeway ramp metering from Signal Temporal Logic specifications. ACC 2017: 4884-4889 - [c274]Ruoxi Jia, Roy Dong, Shankar Sastry, Costas J. Spanos:
Optimal sensor-controller codesign for privacy in dynamical systems. CDC 2017: 4004-4011 - [c273]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Statistical estimation with strategic data sources in competitive settings. CDC 2017: 4994-4999 - [c272]Kamil Nar, Lillian J. Ratliff, Shankar Sastry:
Learning prospect theory value function and reference point of a sequential decision maker. CDC 2017: 5770-5775 - [c271]Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry:
Gradient-based inverse risk-sensitive reinforcement learning. CDC 2017: 5796-5801 - [c270]Ruoxi Jia, Roy Dong, S. Shankar Sastry, Costas J. Spanos:
Privacy-enhanced architecture for occupancy-based HVAC Control. ICCPS 2017: 177-186 - [c269]Daniel J. Calderone, S. Shankar Sastry:
Markov decision process routing games. ICCPS 2017: 273-279 - [c268]Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan:
Pragmatic-Pedagogic Value Alignment. ISRR 2017: 49-57 - [c267]Daniel J. Calderone, Roy Dong, S. Shankar Sastry:
External-cost continuous-type wardrop equilibria in routing games. ITSC 2017: 1-6 - [c266]Daniel J. Calderone, Shankar Sastry:
Infinite-horizon average-cost Markov decision process routing games. ITSC 2017: 1-6 - [c265]Dorsa Sadigh, Anca D. Dragan, Shankar Sastry, Sanjit A. Seshia:
Active Preference-Based Learning of Reward Functions. Robotics: Science and Systems 2017 - [i38]Joshua Achiam, Shankar Sastry:
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning. CoRR abs/1703.01732 (2017) - [i37]Roy Dong, Eric Mazumdar, S. Shankar Sastry:
Optimal Causal Imputation for Control. CoRR abs/1703.07049 (2017) - [i36]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Statistical Estimation with Strategic Data Sources in Competitive Settings. CoRR abs/1704.01195 (2017) - [i35]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, S. Shankar Sastry, Costas J. Spanos:
A Robust Utility Learning Framework via Inverse Optimization. CoRR abs/1704.07933 (2017) - [i34]Katherine Rose Driggs-Campbell, Roy Dong, S. Shankar Sastry, Ruzena Bajcsy:
Robust, Informative Human-in-the-Loop Predictions via Empirical Reachable Sets. CoRR abs/1705.00748 (2017) - [i33]Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire J. Tomlin, S. Shankar Sastry:
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes. CoRR abs/1707.05714 (2017) - [i32]Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan:
Pragmatic-Pedagogic Value Alignment. CoRR abs/1707.06354 (2017) - [i31]Oladapo Afolabi, Katherine Rose Driggs-Campbell, Roy Dong, Mykel J. Kochenderfer, S. Shankar Sastry:
People as Sensors: Imputing Maps from Human Actions. CoRR abs/1711.01022 (2017) - 2016
- [b2]René Vidal, Yi Ma, S. Shankar Sastry:
Generalized Principal Component Analysis. Interdisciplinary applied mathematics 40, Springer 2016, ISBN 978-0-387-87810-2, pp. 1-566 - [j105]Ehsan Elhamifar, Guillermo Sapiro, S. Shankar Sastry:
Dissimilarity-Based Sparse Subset Selection. IEEE Trans. Pattern Anal. Mach. Intell. 38(11): 2182-2197 (2016) - [j104]Samuel A. Burden, S. Shankar Sastry, Daniel E. Koditschek, Shai Revzen:
Event-Selected Vector Field Discontinuities Yield Piecewise-Differentiable Flows. SIAM J. Appl. Dyn. Syst. 15(2): 1227-1267 (2016) - [j103]Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry:
On the Characterization of Local Nash Equilibria in Continuous Games. IEEE Trans. Autom. Control. 61(8): 2301-2307 (2016) - [j102]Insoon Yang, Samuel A. Burden, Ram Rajagopal, S. Shankar Sastry, Claire J. Tomlin:
Approximation Algorithms for Optimization of Combinatorial Dynamical Systems. IEEE Trans. Autom. Control. 61(9): 2644-2649 (2016) - [j101]Pushkin Kachroo, Shaurya Agarwal, Shankar Sastry:
Inverse Problem for Non-Viscous Mean Field Control: Example From Traffic. IEEE Trans. Autom. Control. 61(11): 3412-3421 (2016) - [j100]Pushkin Kachroo, Shankar Sastry:
Travel Time Dynamics for Intelligent Transportation Systems: Theory and Applications. IEEE Trans. Intell. Transp. Syst. 17(2): 385-394 (2016) - [j99]Pushkin Kachroo, Shankar Sastry:
Traffic Assignment Using a Density-Based Travel-Time Function for Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. 17(5): 1438-1447 (2016) - [c264]Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths:
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. AAMAS 2016: 940-948 - [c263]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, Costas J. Spanos, S. Shankar Sastry:
Inverse modeling of non-cooperative agents via mixture of utilities. CDC 2016: 6327-6334 - [c262]Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry:
Understanding the impact of parking on urban mobility via routing games on queue-flow networks. CDC 2016: 7605-7610 - [c261]Aron Laszka, Waseem Abbas, S. Shankar Sastry, Yevgeniy Vorobeychik, Xenofon D. Koutsoukos:
Optimal thresholds for intrusion detection systems. HotSoS 2016: 72-81 - [c260]Shromona Ghosh, Dorsa Sadigh, Pierluigi Nuzzo, Vasumathi Raman, Alexandre Donzé, Alberto L. Sangiovanni-Vincentelli, S. Shankar Sastry, Sanjit A. Seshia:
Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications. HSCC 2016: 31-40 - [c259]